This project detects structural network anomalies using a GNN autoencoder. It contrasts this deep learning approach with the classic DBSCAN method. While DBSCAN only uses node features (CPU, RAM), the GNN learns the graph's topology to identify statistically improbable links, proving superior for structural analysis.

SCI research article - Structural Knowledge-Based Anomaly Detection to Inspect Ball-Based Lens Actuators